The given text mentions about the process of building an LLM-powered analyst and trying different agent types for data analysis tasks. It covers creating agents to interact with an SQL database and using LangChain tools to achieve this. The text explains the process of communicating with, reasoning, and planning for data tasks along with results provided by the model. Additionally, it emphasizes using LangChain’s high-level functions to simplify the process and includes a summary of the different agent types tried.
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Part 2: Diving deeper into LLM agents
In the previous article, we’ve started building an LLM-powered analyst. We decided to focus on descriptive analytics and reporting tasks since they are the most common for analysts. Most analysts start their careers with such tasks, and most companies start building the analytical function with reporting and BI tools.
Setting up a database
First, let’s set up a database we will be interacting with. My choice is ClickHouse. ClickHouse is an open-source column-oriented SQL database management system for online analytical processing (OLAP). It’s a good option for big data and analytical tasks.
Agents overview
The core idea of the LLM agents is to use LLM as a reasoning engine to define the set of actions to take. In the classic approach, we hardcode a sequence of actions, but with agents, we give the model tools and tasks and let her decide how to achieve them.
Building Agent from Scratch
Let’s start to build an agent. We will do it from scratch to understand how everything works under the hood. Then, we will use LangChain’s tools for faster prototyping if you don’t need any customization.
Different Agent Types
We’ve built an LLM agent based on OpenAI functions from scratch. However, there are quite a lot of other approaches. So let’s try them out as well.
Do we need to build everything from scratch?
We’ve spent some wonderful learning time building the agent integrated with SQL Database. However, I must mention that LangChain has its own implementation of SQL agent — SQLDatabaseChain.
Thank you a lot for reading this article. I hope it was insightful to you. If you have any follow-up questions or comments, please leave them in the comments section.
Can LLMs Replace Data Analysts? Getting Answers Using SQL was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Can LLMs Replace Data Analysts? Getting Answers Using SQL
- Towards Data Science – Medium
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